Assessing multiparametric drug response in tissue engineered tumor microenvironment models.

The tumor microenvironment is important in promoting treatment resistance of tumor cells via multiple mechanisms. However, studying this interaction often proves difficult. In vivo animal models are costly, time-consuming, and often fail to adequately predict human response to treatment. Conversely, testing drug response on human tumor cells in vitro in 2D cell culture excludes the important contribution of stromal cells and biophysical forces seen in the in vivo tumor microenvironment. Here, we present tissue-engineered models of both human brain and breast tumor microenvironments incorporating key stromal cell populations for assessing multiple mechanisms of therapeutic response using flow cytometry. We show our physiologically-relevant systems used to interrogate a variety of parameters associated with chemotherapeutic efficacy, including cell death, proliferation, drug uptake, and invasion of cancer and stromal cell populations. The use of flow cytometry allows for single cell, quantitative, and fast assessments of multiple outcomes affecting anti-tumor therapy failure. Our system can be modified to add and remove cellular components with ease, thereby enabling the study of individual cellular contributions in the tumor microenvironment. Together, our models and analysis methods illustrate the importance of developing fast, cost-effective, and reproducible methods to model complex human systems in a physiologically-relevant manner that may prove useful for drug screening efforts in the future.

Designing Patient-Driven, Tissue-Engineered Models of Primary and Metastatic Breast Cancer.

The rising survival rate for early-stage breast cancer in the United States has created an expanding population of women in remission at risk for distant recurrence, with metastatic spread to the brain demonstrating an especially poor prognosis. The current standard of care for breast cancer brain metastases is not well defined or differentiated from the treatment of brain metastases from other primary sites. Here, we present tissue-engineered models of the primary and brain metastatic breast cancer microenvironments informed by analysis of patient tumor resections. We find that metastatic resections demonstrate distinct cellular and matrix components compared with primary resections or non-cancerous controls. Using our model systems, we find that the observed deposition of collagen I after metastasis to the brain may enhance breast cancer invasion. Future optimization of these models will present a novel platform to examine tumor-stroma interactions and screen therapeutics for the management of metastatic breast cancer.

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections.

With the growing interest in the tumor microenvironment, we set out to develop a method to specifically determine the microenvironment components within patient samples of glioblastoma, the deadliest and most invasive brain cancer. Not only are quantitative methods beneficial for accurately describing diseased tissues, they can also potentially contribute to more accurate prognosis, diagnosis, and the development of tissue-engineered systems and replacements. In glioblastoma, glial cells, such as microglia and astrocytes, have been independently correlated with poor prognosis based on pathologist grading. However, the state of these cells and other glial cell components has not been well-described quantitatively. This can be difficult due to the large processes that mark these glial cells. Furthermore, most histological analyses focus on the overall tissue sample or only within the bulk of the tumor, as opposed to delineating quantifications based on regions within the highly heterogeneous tissue. Here, we describe a method for identifying and quantitatively analyzing the populations of glial cells within the tumor bulk and adjacent regions of tumor resections from glioblastoma patients. We used chromogenic immunohistochemistry to identify the glial cell populations in patient tumor resections and ImageJ to analyze percent coverage of staining for each glial population. With these techniques we are able to better describe the glial cells throughout regions of the glioma tumor microenvironment.

Quantitative Analysis of the Cellular Microenvironment of Glioblastoma to Develop Predictive Statistical Models of Overall Survival.

Glioblastomas, the most common primary malignant brain tumors, have a distinct tissue microenvironment. Although non-neoplastic cells contribute to glioblastoma progression, very few quantitative studies have shown the effect of tumor microenvironmental influences on patient survival. We examined relationships of the cellular microenvironment, including astrocytes, microglia, oligodendrocytes, and blood vessels, to survival in glioblastoma patients. Using histological staining and quantitative image analyses, we examined the tumor-associated parenchyma of 33 patients and developed statistical models to predict patient outcomes based on the cellular picture of the tumor parenchyma. We found that blood vessel density correlated with poorer prognosis. To examine the role of adjacent parenchymal versus higher tumor cell density bulk parenchymal tissue, we examined the glial components in these highly variable regions. Comparison of bulk and adjacent astrocytes and microglia in tissue yielded the strongest prediction of survival, with high levels of adjacent astrocytes predicted poor prognosis and high levels of microglia correlated with a better prognosis. These results indicate that parenchymal components predict survival in glioblastoma patients and in particular that the balance between reactive glial populations is important for patient prognosis.